 So, welcome everyone to this parallel session on building resilient e-addisar systems in Sierra Leone. My name is Johan Sade from the University of Oslo and I'll moderate this session as as far as practically possible. We have two presenters for this session. I will just briefly introduce them before I'll hand over the floor to the presenters. So first we have Richard Magoba from Affinett. She's a public health informatics specialist with four years experience in disease surveillance information system and she has been involved in setting up e-addisar system in Sierra Leone. The second presenter is Kalle Hedberg from South Africa. As you can see, he has a long experience in the field of HIS and also a pilot known as a pilot in his network. So without much more ado, I'll give the word to Bridget and we have around 30 minutes for Bridget and Kalle to present. Any questions if you can write those in the community of practice, we will bring them up after the presentations. So Bridget, please you can go ahead and share your presentation now. Okay, thank you Johan. I'll share my screen. Can everyone see my screen? Yes. Good afternoon. Good morning. I'm Bridget Magoba from Affinett, Sierra Leone. I'm presenting on the electronic aspect surveillance system in Sierra Leone. Just to give a brief, in Sierra Leone, we currently have the weekly surveillance reporting system which is aggregate, then also the case-based electronic disease surveillance system. So I'm going to mainly focus on the case-based disease surveillance system. So in Sierra Leone, the Ministry of Health, with its partners CDC, HIS South Africa, Affinett have set up the electronic case-based surveillance system which reports 20 plus notifiable diseases. So this was set up since 2018 with support from HIS South Africa as Kalle will also add on. In the initial stages, we developed the enrolment. It is a tracker program, so it has several stages as we're going to see the next slides. Then the system went through several revisions through partners WHO and CDC with the Ministry of Health, another directorate of health security and emergencies, and then it was piloted in four districts out of 16. In February when COVID outbreak came up, we expanded on the very existing case-based surveillance system that we had and incorporated COVID-19 as one of the notifiable medical conditions in the country. So in Sierra Leone, we did not develop a new instance for COVID-19. We built on what was already existing. So during the pandemic, the Ministry of Health relied on the case-based surveillance for information management, which was rolled out in the 16 districts and it was what is being reported for report for COVID-19 data up to date. Currently, we are expanding the system to the other districts for the other notifiable diseases besides COVID-19. So this was a lesson from the Ebola outbreak, where we needed to use the existing applications to build on other new information systems or other new diseases that come up. So as I mentioned earlier, it's a tracker program that has different stages. One is the case registration, which is the enrollment, and this is mainly captured by the health facility staff using mobile devices. In Sierra Leone, we are using tablets. During the weekly disease surveillance reporting, which was rolled out the entire country, each health facility received a tablet for reporting. So with the electronic space disease surveillance, it was easier for us to use the existing devices, the existing resources for us to roll out this system. So the facilities already have the tablets and those are the resources that we are using for data capture at facility level. Then we have the lab request and the lab result stage. That request mainly is captured by the district health management team. While the lab results stage is mainly captured by the laboratory technicians from the different testing labs. Then we have different case investigation forms. Each disease has a case investigation form besides some diseases that are classified like for viral hemorrhagic fevers classified in Ebola and other diseases. Then we also have final outcome that closes the entire case. So this is the entire flow of the program. So this is the data flow of the case-based disease surveillance system. Everything starts from the health facility and the health facility notifies the case using a tablet that is the Android application. So then a notification is sent through SMS and email to the district health management team and they fill a lab request form based on what they find on ground when they visit the facility. Then depending on what assessment they've made and then they'll have to fill the lab request form as well as capture it in the system. At the district health management team they're able to use either the android application or the web application on the computer because they have access to both the tablet and a computer we'll have to. So they go ahead and also capture information on the case investigation based on the disease that is being reported. Then the lab that receives the sample also captures results into the system after analyzing the sample that has been collected. Though we still have some gaps on the results capture the lab is still resisting on capturing lab results directly into the system and currently we receive results through Excel files. Then the surveillance team will be the one to capture the lab results directly into the system from the Excel files. Then the national level analyzes the data that is sent from the different reporting levels for decision making at the emergency operation center as well as sharing information with other ministers of health departments and other ministries in the country. So as I mentioned that we use the existing case-based disease surveillance system to cover COVID-19 in February this year. So COVID-19 as because it is a respiratory illness it was categorized under the group of acute respiratory illnesses where we have the SARS, the MERS and influenza. So that's where we categorize COVID-19 and we use the WHO existing forms the investigation forms the contact tracing forms then that is what we configured into the system to meet the reporting needs for COVID-19 and initially it had questions of animal markets contacts with animals which eventually became irrelevant in Sierra Leone. So we went on revising the form eventually we had to capture in some other sections that are relevant in Sierra Leone aspect compared to the general WHO assessment. So we also added the contact tracing and quarantine monitoring tool which is still being used right now so we have two programs we have the NMC which is covering the information on the COVID-19 investigation then we also have the contact tracing which is a separate program and contact monitoring then we also have the port of entry passenger locator event we were using this for air travelers which we are going to expand to land travelers as well currently Sierra Leone is planning to open up land borders so we are going to use the same event program to capture data on on travelers through land currently we are using it for travelers through air also we are using it before at the beginning of the outbreak before the airport closed so it was very informative for us to tell where the cases were coming the confirmed cases we are traveling from and so on and so forth including quarantine monitoring so we also have a program on quarantine site monitoring and this is an integration from another application called com care which has been introduced by the directorate of science and technology um so um this program pulls data from the com care app into um the the tracker program in in dh is to which gives us information on the different quarantine sites um during this outbreak so this is a multiple data flows into um electronic space this is a surveillance that we have currently for COVID-19 um it all starts from uh from a suspected case which might be reported either through a health facility or through a toll free line um from the community which is free to everyone in the in Sierra Leone then um then it gets to the case investigation team and the case investigation team will have to to get on ground either the health facility all in the community and fill the case investigation form which was already configured in um in dh is to then the data team goes ahead and captures this information into the system um same applies to quarantine um before I move to quarantine the case investigation team um will have to as well line list um contacts of the case that has been identified then um the sample will be collected and sent to the lab so with that uh when lab results return as I mentioned earlier that we still have a gap with the laboratory team the laboratory team um does not capture the results directly into the system but they share the results in excel so the surveillance data team goes ahead and captures the results in the system on behalf of the laboratory technician so that is still a gap on our side um but we are still pushing on to um encourage the the lab team to capture the results directly into the system and also looking at the workload um during the outbreak the lab team is so strained and stretched um to the extent that they are unable to to capture data directly into into the system so again uh when it's when I go back to the floor we also have the quarantine team that has to keep the to pick the contact list from the system that was already captured from the uh by the data team so this is what they use to keep monitoring um the contacts that are in quarantine uh until they complete the 14 days so in addition there's also a small daily aggregated data from com care that I mentioned in the previous slide and that we can also use to assess how many um how many contacts are quarantine in the specific quarantine sites um then we also have the case management team the case management team updates data on uh on the admission information of the cases and also um outcome information of the cases deaths recoveries evacuations and any other um clinical best information from their end so we have um some key achievements and challenges that we we have um we're able to capture information on uh the confirmed probable suspected cases on time um because the issue district is able to capture this information on their end and national level um analyzes and is able to make decisions based on that data there are minor discrepancies between the numbers that we have in the system vis-a-vis what the the situation room um is reporting um manually there's also limited capacity to capture all the negative lab results so uh focus is given um on the positive results then negative um we compare the excel sheets shared by the lab and we're able to import them into the system we've configured key indicators the confirmed cases confirmed deaths confirmed recoveries and so on case facility rates interpret and we're able to respond to any data requests from um from partners and ministry of health as well in relation to to the outbreak um there also no address geodata geodata into the system because data is captured from the hard copies we have a gap of um of uh tablets so um users are not able to collect this information directly from the field so we use addresses that are captured um in the system for us to get the the geolocation and we're able to get the maps of where these cases um are coming from so we uh we also have information on the contacts and quarantine monitoring through the system and this is also done at district level so it is um it is accurate data from the district and we're able to get the transmission chains um from the system with the transmission chains we are also making use of uh of geodata with also the relationship um applications in dh is to to come up with the transmission chains um data integration with also case management for outcome and clinical data this um was um was this started the midway of the outbreak um it wasn't um something that was easy at the start to to incorporate case management data into the existing surveillance data um then we're able to to capture data using android and currently there's a rollout at the facility level that is taking place though we still have challenges with the sms channel um submission so we are relying on zero rating the instances um for for for submission email and sms notifications are also active at the different reporting levels and we've also deployed the system for COVID-19 in all the 16 districts so all that 16 districts are able to report um COVID-19 suspected probable and confirmed cases including contacts and quarantine um cases uh contacts from their different districts but the challenge we have is um some facilities have all devices that uh 4.4 4.4 KitKat which uh which we are facing challenges with um using the application then we've also had the mobile device management previously we are 80 percent of the devices we are configured however it was getting it was becoming costly for government and now we've resorted to using up lock which is uh which is which can't be accessed remotely so we still have that gap and we have visualizations with uh with data from the ports of entry with data from case based um surveillance contact tracing and line listing um the traveler quarantine follow-up and case management we are able to pull out information um such visualization the maps the tables the graphs um and transmission chains as well then we also have displays we are making use of uh the display application which was uh developed by his Uganda so we display on the screens at the emergency operation center including the the situation room of um of the response so they're able to see up to this information from time to time um some examples of data use include we've um we've used the reports the maps the charts um in the situation room to make um decisions we are using the same information to external portals so um external portals with um directorate of policy planning and innovation pulls data from the case based disease surveillance system um on um on the from the data of COVID-19 then we use that data to also determine the hotspot areas for surge for sentinel surveillance and monitor the transmission chains as well um data trans data dissemination also to other ministries for example ministries of gender for inclusion of gender um in the treatment centers and also um in quarantine and same applies to a directorate of nutrition to um to include um child food and diet to consideration of the treatment centers and uh for quarantine homes and facilities and also a psychosocial support um this data is also used to make public health decisions like like opening schools and um and so on so the next steps with the ACBDS rollout is we're looking at the transmission chain analysis using the relationship app supporting multiple tracker uh programs so as I mentioned earlier this one we are looking at uh using two applications DHS2 and GoData for comparison and uh we are also looking at including clinical data from case management currently um what we have is the out the outcome information that dates the recoveries and so on we're also looking at operational data from case management the bed capacity bed utilization and the admission rate and so on um then also the com care application which we've already started um the integration this is something that we are also continuing with so that we are able to have data from com care application flowing to um ACBDS then integrating the E-man first application which was developed by another by another agency into ACBDS so the main aim here is that we are focusing on having um this system be the center for COVID-19 data so any other um small applications we integrate them into uh we integrate them to ACBDS so that it becomes the central um the central data for COVID-19 then we are also looking at a possible inclusion of key logistics data like masks availability of PPE drugs and so on um we're also looking at uh tracking other conditions like maternal deaths and perineto perineto deaths um as independent programs within the system then also better analytical integration between ACBDS and EIDSR EIDSR is the weekly aggregate data which is already rolled out throughout the entire country in the 16 districts and they've been reporting for the past um one and a half years through that platform so we are looking at comparing the cases that are being reported um within the case-based disease surveillance um vis-a-vis what is reported on the weekly basis um and also um including the disease-related monthly aggregate data um thank you thank you Richard um I'll now give the word to Kalle and remember you can you can write your questions at the community practice uh if you do that it's also a possibility for us to get back to these questions uh later also after the presentations and the conference all right good afternoon everybody this is Kalle here talking from Oslo are you able to see my screen yes yes all right so I have only about five minutes so I will mostly talk about the more sort of strategic uh aspects of this development now at the beginning of the COVID pandemic what was interesting was that in almost all the webinars that I took part in everybody with experience particularly from the Ebola epidemic was stressing the need to use existing systems and that was a hard one experience during Ebola we saw how an epidemic or a pandemic with a high public profile ends up being a proliferation grenade or a fragmentation grenade with lots and lots of different actors and stakeholders jumping in and saying we have the solution for this we have the solution for that and the net result of it is that the uh the um both the public focus and the the management focus tend to be sort of diversified people are tracing up all kinds of avenues and very often the basic methodologies that are known to work in which the health sector is familiar with ends up actually suffering and not getting the support that they require uh I'm also going to talk a little bit about integration between IDSR which is integrated disease surveillance and and response and um and yeah some of the implications for DHIs too so the problem we have seen and I guess most of you are pretty familiar with the the the scene in the United States which is in a way extreme because the US have gone from a situation where they during Ebola actually had a very coordinated and concerted effort and I think played an absolutely vital role particularly the US Armed Forces but also CDC in assisting the three countries in West Africa to to beat down and and finally get rid of the the epidemic they've gone from that to a completely I would say almost chaotic situation where politicians where scientists where all kinds of conspiracy theories etc is constantly creating a scene of chaos and pulling also the population in all kinds of of directions and I want to go into details on that but you've seen similar things in a lot of other countries to a greater and lesser extent few countries Sierra Leone for instance Norway for instance considerably better I would say than average but even here you've seen some of the same things about you know new apps being thrown out as the solution to things and then after two or three or four weeks it turns out to not work you obviously have consultant companies you have all kinds of app developers for the best of of intentions you know pushing their apps and seeing it as a way of I would say making themselves known as a way of marketing and finally which we have seen to some extent in in Sierra Leone I'd say is that within ministries and within sections of ministries these pandemics ends up being also venue for sort of turf force different bodies are all paying lip service to full integration and full coordination etc but in practice they are actually doing their own things and and you know waving the flags Sierra Leone we've we've seen a number of new apps and whatever being pushed out and even if so far at least everybody is saying that well the data should go into the cbds but so far it hasn't and again it comes back to this this fact that during normal times you see surveillance particularly low income countries but also in wealthier countries is not seen as a very high priority it's seen as boring it's a lot of drudgery you know you're you're monitoring things on a on a day by day basis for years and years suddenly something happens so we've seen it in Europe for instance we're very very very few countries have still maintained their large the depots for instance of all PPEs and other kinds of equipment most of them found that to be not cost effective so they can rid of it then you have a pandemic and you are in a crisis mode everybody is screaming and fighting over the same resources so the little bit of the background was then that the cbds was based on the template that I was part of developing for South Africa which is used there for malaria at the moment based on the fact that normally a notifiable diseases will have one common registration form for all diseases and they would typically have one sort of process for dealing with laboratory data and getting laboratory results back but then it diversifies based on the disease although again we we decided in Sierra Leone that most of the acute respiratory infections are so similar that we can group them together and you can do that with some other diseases but typically you have a diversification and in tracker program terms that means that you have multiple case investigation stages basically one for each disease because what you are investigating will vary depending on on the disease and but it was the whole design has been that it's very easy to add new diseases it's easy to add new processes follow-ups like like follow-up visits and so forth because you can just add another stage you can add a few data elements etc so it was designed to actually grow over time because we know that you know since 2000 we've had SARS, MERS, Ebola, smet flu, a range of zoonotic diseases particularly but new diseases and we know experts estimate there's around 40 000 viruses in the animal world some of them will definitely jump to humans become zoonotic diseases so you know it just doesn't make any sense that with every new zoonotic disease we end up having to create a new set new databases new tools new everything it just don't make any sense and I think it's also often underestimated the strain this poses on the staff because again one of the things we've seen in Sierra Leone because we were we had just sort of piloted the ECBDS when the pandemic struck was that we had huge we've had huge problems weaning people off just using their own excel sheets we in sort of parallel to the ECBDS capturing all the data and providing it and increasingly taking over people have been running all kinds of excel sheets you know in in sort of parallel which we are only slowly then managing to to to pull in another future aspect which we know gonna gonna grow stronger is all the ideas around big data social data mining all kinds of variable devices etc etc right these torrento technology solutions will just increase but we also see that few of them live up to expectations at least for now right there's a lot of hype very little actual impact on we see now with the pandemic most countries ends up predominantly relying on contact tracing on following up on treating people efficiently etc and on traditional methods like social distances wearing masks etc right so these fancy new technical solutions generally don't have much impact on the epidemic it might change in the future but for now it's really really critical that we establish integrated systems that can be maintained that are not too fragmented you know so if you're if you're sitting with five or six or seven or eight databases is much more resource intensive than if you're sitting with one or two so we need to maintain this sorry may i ask you to wrap it up now leave some time for questions please yeah and and the the critical part here i think is in addition to maintaining an integrated ideas our system is also to start more actively combined surveillance data and normal service data which is traditionally monthly and aggregated but increasingly also might be tracker type of data and it's critical i think going forward to better uh shall we say design the health data so that it's possible it's it's more possible it's easier to combine that with social and economic data to assess impact of different interventions that you're doing in a pandemic situation and the implication for dhs2 that's my last slide here is that it fits as an integrated platform these key requirements but the current data capture is not optimal for ideas are we are working hard on trying to to to shall we say fix that with a new app trap more possibilities for line listing type of editing and a range of other things that makes it easier to use for these specific things we're still having some challenges with like the sms channel on android but again that's also being resolved and we are working on improving analytics and and line listing what i think we are missing is better tools for communicating this surveillance data with population so not only to other experts it's as i said integration with social economic models and it is generally to increase the awareness among decision makers and politicians that health and disease is equal to security and it needs the same time of long-term investments as as politicians general are very happy to put into you know military hardware and that kind of thing thank you okay thank you bridge it and color we have time we have five minutes it should quit a bit early to leave time for people to arrange the next session but i'm going to share my screen now and show um if you stop sharing your screen color there are some questions on the community practice feel free to add more there if i share my screen and um you should be able i hope now to see some questions and i believe we can start at the at the top and there's a question here i hope you see it bridge it from surva dal nielsen you mentioned resistance among staff to record the datum system can you say a bit more about that did you investigate the reasons for this thank you dalsen one is they they are resisting change they feel more comfortable using the excel sheets that they've uh that they've been using um despite several trainings that they go through they feel more comfortable um capturing data on the excel sheets but also connectivity um could also be a challenge um because we have labs um the district not in the capital city as well so um the fact that we don't have the sms channel working yet um data bundles becomes a challenge um that's why i mentioned that we are looking at zero rating the the the instances so that this can no longer be a challenge um but also they feel they feel um they are taking a lot of time um capturing data through the the the the the web best system as well as um they need to maintain their excel sheets so they're looking at also maintaining uh parallel systems um one other thing that they that were raising was um um confidentiality of patient information um which we we we we explain to them that this data is only accessed by people who have um access to it um who need to have access to it so as as we move on we are we are able to keep explaining and you know um convincing them to to accept that change from excel to um to the web best platform so so further that improvement um um compared to how we started if i can add something here also comment on some of the other questions let me say resistance to change is part of the reason we also saw when traveler started paying for um for lab tests that the the lab tended to prioritize that so there might be you know money might play a role here too it's often one of the reasons why different sections want their own systems because it's a way of getting additional resources but in all fairness uh last week when somebody was sort of questioning the labs uh why they were resistant to capturing data directly the response was quite swift and the person was invited to come to the lab dress up in full PPE gear spend a few hours in a lab that deals with live Ebola viruses and live COVID viruses and see how easy it is uh with with hands in gloves and whatever to actually use the current technology we have with the touchscreens and whatever so there is also an aspect of that I don't think most of our tools are very suitable for hands in three layers of gloves etc we also know if you have have gloves on how difficult it is to even you know deal with your own smartphone Eric is asking about the number of cases well we're talking 15 to 20,000 cases I know that I have so far you located about 12,000 between 12 and 13,000 cases so totally we're talking close to 20,000 obviously a lot less than some countries but also not not small numbers for a disease surveillance system and bram is asking about aggregated weekly report and case-based report data and for now that's definitely happening in parallel and the data is not partially it's used to identify gaps and whatever but it's also not directly relatable because the weekly aggregated data are suspects whereas in the case-based system we are obviously wanting to know which of the suspect cases are confirmed cases and this is the same for malaria systems you're normally focusing predominantly on confirmed cases but over time I think those of us who look some years ahead here we can see that for all diseases with reasonable numbers and that means up to two three four five thousand cases maximum per year it makes sense to move to case-based disease surveillance and then gradually phase out the the weekly aggregate stress becomes aggregates for the for the case-based data but for for some like malaria in Sierra Leone is also notifiable and they have you know two three million cases or more a year there is no way that that it doesn't make sense to have that as as for case-based disease surveillance because there is no capacity to investigate and no meaning really it has it doesn't really have any meaning to investigate other countries like South Africa which has 10 000 cases they that's all case-based and not no no aggregated data at all we're doing completely away with the aggregated data collection so this is a process and I would expect it to take two three four years thank you okay Kalle thank you I think we have to stop there but please keep adding questions if you have and I encourage Bridget and Kalle and others to to visit this page on the community of practice again to follow up on this discussion thank you to everyone who came to listen to this and the big special thanks to Bridget Magov and Kalle Hedberg for presenting and I'll see you in the next session okay thank you everyone